3D Human pose estimation: A review of the literature and analysis of covariates

By Nikolaos Sarafianos, Bogdan Andrei Boteanu, Bogdan Emanuel Ionescu, Ioannis A. Kakadiaris. 2016

331 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, 3D Vision & Pose

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In 3D Vision & Pose

Citations in the corpus

Interpretable 3D Human Action Analysis with Temporal Convolutional Networks (2017, background): “Given the recent progress of powerful human pose estimators from RGB or RGBD data [27], human activity recognition model that builds on top of 3D skeletons is a promising direction.”
Weakly-supervised Transfer for 3D Human Pose Estimation in the Wild (2017, background): “Human pose estimation has been studied considerably in the past [17, 23], and it is beyond the scope of this paper to provide a complete overview of the literature.”
Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach (2017, background): “Human pose estimation has been studied considerably in the past [16, 23], and it is beyond the scope of this paper to provide a complete overview of the literature.”
On Self-Contact and Human Pose (2021, background): “Despite rapid progress in 3D human pose estimation [19, 20, 23, 31, 34, 40, 45], and despite the role that self-contact plays in our daily lives, only a handful of previous works discuss selfcontact.”
You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions (2019, background): “Third-person body pose and interactions There is extensive literature on human body pose estimation from the traditional third-person viewpoint, where the person is entirely visible [53, 50, 64, 13].”
Deep 3D human pose estimation: A review (2021, unknown): “(Sarafianos et al., 2016) note that the temporal information may not be well utilized.”
MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior (2017, unknown): “Considerable research has addressed the challenge of human motion capture from imagery [13], [14], [15], [16], [17].”
Deep Learning-based Human Pose Estimation: A Survey (2020, methodology): “A survey of human pose estimation: the body parts parsing based methods [9] 2015 JVCIR A survey of body parts parsing-based methods for human pose estimation under single-view and multiple-view settings with different input sources (images, videos, and depth) Human pose estimation from monocular ima”
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose (2016, background): “Here, we survey works that are most relevant to ours with a focus on ConvNet-based approaches; we refer the reader to a recent survey [29] for a more complete literature review.”
Single-Shot Multi-person 3D Pose Estimation from Monocular RGB (2017, unknown): “[50] provide a more comprehensive review.”
Resolving 3D Human Pose Ambiguities With 3D Scene Constraints (2019, background): “The community has made significant progress on estimating human body pose and shape from images [18, 43, 53, 60].”